Summary

Dataset 1

Experiments excluded

Mask

Get figure file: figures/preliminary_dset-1_figure-mask.png

Peak coordinates

Get figure file: figures/preliminary_dset-1_figure-static.png
Get figure file: figures/preliminary_dset-1_figure-legend.png

Explorer

Meta-Analysis

Estimator

Parameters use to fit the meta-analytic estimator.

Corrector

Parameters use to fit the corrector.

Corrected meta-analytic map: z_corr-FDR_method-indep

Explorer

The following figure provides an interactive window to explore the meta-analytic map in detail.

Slice viewer

This panel shows the the corrrected meta-analytic map.

Get figure file: figures/corrector_figure-static.png

Diagnostics

Target image: z_corr-FDR_method-indep

Significant clusters

    X Y Z Peak Stat Cluster Size (mm3)
Tail Cluster ID          
Positive 1 -42.00 20.00 -8.00 6.79 16160
1a -46.00 28.00 -4.00 5.66
1b -34.00 20.00 -10.00 4.60
1c -36.00 12.00 -4.00 4.60
2 38.00 20.00 -4.00 5.90 7592
2a 32.00 20.00 2.00 4.32
2b 48.00 26.00 0.00 3.72
2c 36.00 18.00 6.00 3.40
3 -8.00 -58.00 28.00 5.66 6160
3a -2.00 -56.00 36.00 4.32
3b -10.00 -50.00 20.00 3.06
3c -2.00 -62.00 42.00 2.36
4 -4.00 18.00 52.00 5.66 27408
4a -4.00 54.00 4.00 5.41
4b -6.00 44.00 0.00 5.15
4c 0.00 52.00 8.00 5.15
5 -52.00 -60.00 28.00 5.15 12792
5a -58.00 -38.00 2.00 4.02
5b -46.00 -62.00 32.00 4.02
5c -48.00 -56.00 38.00 3.72
6 -46.00 6.00 48.00 4.88 3040
6a -36.00 4.00 56.00 2.01
7 50.00 -28.00 2.00 4.32 5480
7a 54.00 -20.00 -4.00 4.02
7b 50.00 -32.00 -8.00 3.40
7c 60.00 -8.00 -10.00 3.40
8 -16.00 12.00 10.00 3.72 1000
8a -8.00 10.00 4.00 2.36
8b -24.00 8.00 8.00 2.01
9 -42.00 -36.00 52.00 3.72 2208
9a -50.00 -34.00 42.00 3.06
9b -38.00 -38.00 52.00 3.06
9c -50.00 -34.00 46.00 2.71
10 6.00 -90.00 -2.00 3.06 1544
10a -4.00 -88.00 0.00 2.71
10b 6.00 -86.00 0.00 2.71
10c -12.00 -90.00 -4.00 2.36
11 2.00 18.00 -4.00 3.06 1760
11a 6.00 6.00 -2.00 3.06
11b -2.00 14.00 -6.00 2.71
11c 6.00 16.00 -8.00 2.71
12 24.00 -2.00 56.00 3.06 992
12a 18.00 4.00 52.00 2.01
12b 20.00 -6.00 60.00 2.01
13 42.00 -64.00 -18.00 3.06 704
13a 40.00 -62.00 -10.00 2.36
13b 42.00 -58.00 -18.00 2.01
14 -50.00 8.00 -24.00 2.71 504
14a -52.00 0.00 -24.00 2.36
14b -50.00 8.00 -30.00 2.36
15 48.00 -62.00 -2.00 2.71 584
15a 46.00 -70.00 8.00 2.36
15b 50.00 -66.00 -2.00 2.36
15c 50.00 -62.00 10.00 2.36
16 -6.00 10.00 0.00 2.71 112
17 18.00 -82.00 -6.00 2.71 368
17a 16.00 -94.00 0.00 2.36
18 -52.00 -54.00 -6.00 2.71 216
18a -46.00 -58.00 -10.00 2.01
19 32.00 -66.00 -20.00 2.71 136
20 -20.00 -52.00 -22.00 2.01 120

Label map: positive tail

Get figure file: figures/diagnostics_tail-positive_figure.png

FocusCounter

The FocusCounter analysis characterizes the relative contribution of each experiment in a meta-analysis to the resulting clusters by counting the number of peaks from each experiment that fall within each significant cluster.

The heatmap presents the relative contributions of each experiment to each cluster in the thresholded map. There is one row for each experiment, and one column for each cluster, with column names being PostiveTail/NegativeTail indicating the sign (+/-) of the cluster's statistical values. The rows and columns were re-ordered to form clusters in the heatmap.

Heatmap: positive tail

Methods

We kindly ask to report results preprocessed with this tool using the following boilerplate.

A multilevel kernel density (MKDA) meta-analysis \citep{wager2007meta} was performed was performed
with NiMARE 0.6.1 (RRID:SCR_017398; \citealt{Salo2023}), using a(n) MKDA kernel. An MKDA kernel
\citep{wager2007meta} was used to generate study-wise modeled activation maps from coordinates. In
this kernel method, each coordinate is convolved with a sphere with a radius of 10.0 and a value of
1. For voxels with overlapping spheres, the maximum value was retained. Summary statistics (OF
values) were converted to p-values using an approximate null distribution. The input dataset
included 1730 foci from 215 experiments. False discovery rate correction was performed with the
Benjamini-Hochberg procedure \citep{benjamini1995controlling}.

Bibliography

@article{Salo2023,
  doi = {10.52294/001c.87681},
  url = {https://doi.org/10.52294/001c.87681},
  year = {2023},
  volume = {3},
  pages = {1 - 32},
  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and Julio A. Yanes and Angela R. Laird},
  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},
  journal = {Aperture Neuro}
}
@article{benjamini1995controlling,
  title={Controlling the false discovery rate: a practical and powerful approach to multiple testing},
  author={Benjamini, Yoav and Hochberg, Yosef},
  journal={Journal of the Royal statistical society: series B (Methodological)},
  volume={57},
  number={1},
  pages={289--300},
  year={1995},
  publisher={Wiley Online Library},
  url={https://doi.org/10.1111/j.2517-6161.1995.tb02031.x},
  doi={10.1111/j.2517-6161.1995.tb02031.x}
}
@article{wager2007meta,
  title={Meta-analysis of functional neuroimaging data: current and future directions},
  author={Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},
  journal={Social cognitive and affective neuroscience},
  volume={2},
  number={2},
  pages={150--158},
  year={2007},
  publisher={Oxford University Press},
  url={https://doi.org/10.1093/scan/nsm015},
  doi={10.1093/scan/nsm015}
}